Forem: Codes Me

I Love Tailwind. Sorry Not Sorry – How Python Can Help Your Workflow

Codes Me — Thu, 07 May 2026 18:13:32 +0000

Introduction

Tailwind CSS has become a game-changer for many web developers, myself included. Its utility-first approach drastically speeds up development and keeps your CSS lean. But even with the best tools, we can always find ways to optimize our workflow. Today, I want to show you how a little Python script can assist in understanding and managing your Tailwind CSS usage, bridging the gap between design and data.

The Problem

When working on large projects, or inheriting an existing codebase, it can sometimes be challenging to get a quick overview of the Tailwind classes being used. You might want to quickly identify common patterns, count unique classes, or even just get a sense of the CSS utility diversity. Manually sifting through HTML files is tedious and error-prone.

The Solution

Python, with its powerful string manipulation and regex capabilities, is perfect for this kind of task. We can write a simple script to parse HTML content, extract all class attributes, and then identify and count the unique Tailwind utility classes. This isn't a full CSS parser, but a practical utility to gain insights into your project's styling footprint. Below is a function that takes HTML content and returns the count of potential Tailwind classes, along with a list of those classes.

import re

def count_tailwind_classes(html_content):
    """
    Parses HTML content to find and count potential Tailwind CSS classes.
    This is a simplified approach, focusing on common Tailwind patterns.
    """
    tailwind_classes = set()
    # Find all class attributes
    class_attributes = re.findall(r'class=["\']([^"\']*)["\']', html_content)

    for classes_str in class_attributes:
        for cls in classes_str.split():
            # A heuristic to identify Tailwind classes:
            # - Contains a hyphen (e.g., 'text-xl', 'bg-blue-500', 'p-4')
            # - Is a common single-word utility (e.g., 'flex', 'block', 'hidden')
            if '-' in cls or cls in ['flex', 'block', 'hidden', 'relative', 'absolute', 'fixed']:
                tailwind_classes.add(cls)
    return len(tailwind_classes), sorted(list(tailwind_classes))

sample_html = """
<div class="flex items-center justify-between p-4 bg-gray-800 text-white">
    <h1 class="text-xl font-bold">My Awesome App</h1>
    <nav>
        <a href="#" class="text-blue-400 hover:text-blue-600 mx-2">Home</a>
        <a href="#" class="text-blue-400 hover:text-blue-600 mx-2">About</a>
    </nav>
</div>
<p class="mt-4 text-gray-700">This is some content without specific tailwind classes.</p>
<button class="btn-primary">Click Me</button> <!-- Not a tailwind class by default -->
"""

num_classes, unique_classes = count_tailwind_classes(sample_html)
print(f"Found {num_classes} potential Tailwind classes in the sample HTML.")
print(f"Unique classes: {', '.join(unique_classes)}")

Result

Running this script on our sample_html will output the number of distinct Tailwind-like classes detected and list them. This gives you an immediate snapshot of your project's CSS vocabulary. While simple, this script can be a foundation for more complex analysis, such as identifying unused classes (though that requires more advanced tooling) or creating a custom style guide based on your actual usage. It demonstrates how Python can be a versatile companion in your front-end development toolkit, even for tasks seemingly unrelated to its core strengths.

Want to build more insightful tools for your web development? Explore Python automation and web scraping techniques at codes-me.com.

🌐 Find me across the web

🌐 Website: https://codes-me.com/
💻 Dev.to: https://dev.to/codes_me_734c93c2eb65de65
🔗 Hashnode: https://hashnode.com/@codes-me
💼 LinkedIn: https://www.linkedin.com/in/alexandre-thiebaut-828133377/
🐦 X: https://x.com/CodesMe344929
🐙 GitHub: https://github.com/codesme34
📺 YouTube: https://www.youtube.com/@CodesMe
👥 Facebook: https://www.facebook.com/Codesme34
🤝 Reddit: https://www.reddit.com/user/codes_me/

How I Built a Real-Time Euro 2026 Score Tracker with Python Automation

Codes Me — Wed, 06 May 2026 18:28:17 +0000

Introduction

The Euro 2026 is heating up, with France in the semi-finals! As a football fan and a Python enthusiast, I wanted a way to stay updated on scores and critical match events without constantly checking my phone. So, I built a simple yet powerful Python bot to do just that. In this article, I'll walk you through the process, from data fetching to real-time notifications.

The Problem

Missing crucial updates during live football matches, or having to manually refresh websites for scores and events. The goal was to create an automated system that provides instant alerts for goals, red cards, or match status changes for the ongoing Euro 2026.

The Solution

I decided to use Python for its versatility in web scraping and automation. The core idea involves periodically scraping a reliable sports data website (like a specific sports news site or a public API if available) for match data, parsing it, and then sending notifications via a platform like Telegram or a custom email. We'll leverage requests and BeautifulSoup for web scraping, and a simple time.sleep for polling intervals. For demonstration, let's assume we're scraping a hypothetical euro2026-scores.com.

import requests
from bs4 import BeautifulSoup
import time

def get_euro_scores():
    url = "https://www.example.com/euro2026-scores" # Replace with actual URL
    headers = {'User-Agent': 'Mozilla/5.0'}
    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status() # Raise an exception for HTTP errors
        soup = BeautifulSoup(response.text, 'html.parser')

        # --- Simplified example for demonstration ---
        # Assume scores are in a div with class 'match-score'
        score_elements = soup.find_all('div', class_='match-score')
        scores = []
        for element in score_elements:
            match_info = element.get_text(strip=True)
            scores.append(match_info)
        return scores
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data: {e}")
        return []

def send_notification(message):
    # Placeholder for actual notification logic (e.g., Telegram bot, email, push service)
    print(f"NOTIFICATION: {message}")

if __name__ == "__main__":
    last_scores = []
    print("Starting Euro 2026 Bot...")
    while True:
        current_scores = get_euro_scores()
        if current_scores and current_scores != last_scores:
            print("New scores detected!")
            for score in current_scores:
                if score not in last_scores: # Only notify for new/changed scores
                    send_notification(f"Euro 2026 Update: {score}")
            last_scores = current_scores
        else:
            print("No new updates.")
        time.sleep(60) # Check every 60 seconds

Result

With this Python script, I now receive real-time updates on Euro 2026 matches directly to my preferred notification channel. No more missing crucial moments or constantly checking multiple sources. This bot efficiently fetches, processes, and alerts, making sure I'm always in the loop.

This simple project demonstrates the power of Python for real-time data monitoring. If you're looking to automate data extraction, monitoring, or any other manual tasks for your business, visit codes-me.com. We offer custom Web Scraping, Automation, and Alert Bots services. Don't forget to check out our free Email Scraper and IP Geolocator tools on codes-me.com for other useful utilities!

7 Python Scripts to Automate Your Daily Tasks in 2026

Codes Me — Tue, 05 May 2026 10:59:02 +0000

Automating repetitive tasks is one of the highest-ROI things you can do as a developer in 2026. Here are 7 ready-to-use Python scripts that will save you hours every week.

1. Bulk Rename Files in One Second

from pathlib import Path

folder = Path("./my_files")
for i, file in enumerate(folder.iterdir()):
    new_name = f"document_{i+1}{file.suffix}"
    file.rename(folder / new_name)
    print(f"Renamed: {new_name}")

Use cases: client photos, CSV exports, monthly reports.

2. Send Automated Emails with Attachments

import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email.mime.text import MIMEText
from email import encoders

def send_email(to, subject, body, attachment=None):
    msg = MIMEMultipart()
    msg["From"] = "you@gmail.com"
    msg["To"] = to
    msg["Subject"] = subject
    msg.attach(MIMEText(body, "plain"))

    if attachment:
        with open(attachment, "rb") as f:
            part = MIMEBase("application", "octet-stream")
            part.set_payload(f.read())
            encoders.encode_base64(part)
            part.add_header("Content-Disposition", f"attachment; filename={attachment}")
            msg.attach(part)

    with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:
        server.login("you@gmail.com", "your_app_password")
        server.send_message(msg)
        print(f"Email sent to {to}")

send_email("client@example.com", "Your Weekly Report", "Hi, please find the report attached.", "report.pdf")

3. Scrape E-Commerce Prices

import requests
from bs4 import BeautifulSoup

def scrape_price(url):
    headers = {"User-Agent": "Mozilla/5.0"}
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, "html.parser")
    title = soup.find("h1").text.strip()
    price = soup.find(class_="price").text.strip()
    print(f"Product : {title}")
    print(f"Price   : {price}")
    return {"title": title, "price": price}

Use cases: competitive monitoring, price alerts.

4. Auto-Generate a PDF Report

from fpdf import FPDF
from datetime import datetime

def generate_report(data: list[dict], filename="report.pdf"):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", "B", 16)
    pdf.cell(0, 10, f"Report — {datetime.now().strftime('%Y-%m-%d')}", ln=True)
    pdf.set_font("Arial", size=12)
    for row in data:
        pdf.cell(0, 8, f"- {row['label']}: {row['value']}", ln=True)
    pdf.output(filename)

generate_report([
    {"label": "Revenue", "value": "$12,340"},
    {"label": "New clients", "value": "47"},
])

5. Monitor a Website and Get Alerts

import requests, time

def monitor(url, interval=60):
    while True:
        try:
            r = requests.get(url, timeout=10)
            status = "Online" if r.status_code == 200 else f"Error {r.status_code}"
        except requests.ConnectionError:
            status = "Offline"
        print(f"[{time.strftime('%H:%M:%S')}] {url} — {status}")
        time.sleep(interval)

monitor("https://codes-me.com")

6. Clean and Deduplicate a CSV File

import pandas as pd

df = pd.read_csv("contacts.csv")
before = len(df)
df = df.drop_duplicates(subset=["email"]).dropna(subset=["email", "name"])
df["email"] = df["email"].str.lower().str.strip()
df.to_csv("contacts_clean.csv", index=False)
print(f"{before - len(df)} rows removed. {len(df)} contacts kept.")

7. Batch Download Files from the Web

import requests
from pathlib import Path

def download_files(urls: list[str], folder="./downloads"):
    Path(folder).mkdir(exist_ok=True)
    for url in urls:
        name = url.split("/")[-1]
        r = requests.get(url, stream=True)
        with open(f"{folder}/{name}", "wb") as f:
            for chunk in r.iter_content(chunk_size=8192):
                f.write(chunk)
        print(f"Downloaded: {name}")

Conclusion

These 7 scripts cover the most common automation use cases. With just a few lines of Python, you can eliminate hours of manual work every week.

Need something more advanced — scraping pipelines, API integrations, real-time dashboards? Check out codes-me.com — we build custom automation tools tailored to your needs.

Which script would save you the most time? Drop it in the comments!

I built a set of free tools !!!

Codes Me — Fri, 01 May 2026 16:48:50 +0000

Here's what's available right now:

1. 📧 Email & Phone Scraper

Paste a list of website URLs, get back emails, phone numbers, and Facebook links — automatically.

What it does:

Scrapes the homepage and the contact page of each URL
Decodes CloudFlare-protected emails (data-cfemail)
Bypasses basic bot protection (curl_cffi Chrome impersonation)
Returns a clean table you can export

Why I built it:
Most scrapers break on Wix/Squarespace sites or miss +33 formatted phone numbers. This one handles split spans, JSON-LD structured data, and obfuscated mailto links.

Free limit: 10 URLs/day — more than enough for prospecting.

2. 🌍 IP Geolocation Tool

Enter any IP address and get detailed info: country, city, ISP, ASN, timezone, VPN/proxy detection.

What it does:

Detects your own IP automatically
Shows an interactive map pinpointing the location
Identifies VPNs, Tor exit nodes, and datacenter IPs
Copy/export results in one click

Useful for debugging regional issues, verifying CDN routing, or checking if a user is behind a proxy.

3. 📊 JSON / CSV → Excel Converter

Upload a JSON or CSV file, get a formatted .xlsx back instantly — in the browser, no server upload.

What it does:

Handles nested JSON (flattens it automatically)
Preserves column types (numbers stay numbers)
Works entirely client-side — your data never leaves your machine

Great for quick data prep before sending to a non-technical client.

4. 📱 QR Code Generator

Generates QR codes for URLs, plain text, emails, phone numbers, WiFi credentials, and vCards.

What it does:

Custom colors and background
Adjustable size and error correction level
Download as PNG instantly

Stack

Backend: Python / Flask, deployed on o2switch (French hosting)
Frontend: React + Tailwind CSS
Scraper engine: curl_cffi for anti-bot bypass + lxml for HTML parsing
Geolocation: ip-api.com
QR: qrcode JS library, runs 100% client-side

Try it

👉 codes-me.com/outils-gratuits

All tools are free, no account required. If you need something custom (higher limits, API access, bulk processing), there's a contact form on the site.

Built by a small digital agency in France. Feedback welcome in the comments.

Stop Getting Blocked: Recon Your Target Website Before Scraping It

Codes Me — Thu, 30 Apr 2026 16:01:46 +0000

The problem

You spend hours writing a scraper, run it, and immediately get a 403.
Or you build it with requests, only to realize the site needs JavaScript to render.

I got tired of this loop, so I built scrapalyser — a Python library that scans
any website before you write a single line of scraper code.

Install

pip install scrapalyser

Usage

import scrapalyser

report = scrapalyser.scan(
url="https://example.com",
output="txt",
lang="en",
)

What it detects

Anti-bot: Cloudflare, DataDome, PerimeterX, Akamai, Kasada, reCAPTCHA, hCaptcha
Tech stack: React, Vue, Angular, Next.js, Nuxt, WordPress, Shopify...
JS required: so you know if requests is enough or if you need Playwright
API endpoints: via CSP headers, inline scripts, or XHR interception (Playwright mode)
robots.txt & sitemap
Login wall: form, redirect, button, OAuth

Two engines

curl_cffi (default): fast, no browser, one HTTP request.

playwright: full browser with XHR interception and screenshot.

report = scrapalyser.scan(
url="https://example.com",
engine="playwright",
headless=False,
screenshot="capture.png",
)

If you get blocked

If the site returns a 403 or a captcha page, the report immediately tells you
which antibot blocked you — all other fields return "blocked by antibot".
No guessing.

Forem: Codes Me

I Love Tailwind. Sorry Not Sorry – How Python Can Help Your Workflow

Introduction

The Problem

The Solution

Result

🌐 Find me across the web

How I Built a Real-Time Euro 2026 Score Tracker with Python Automation

Introduction

The Problem

The Solution

Result

7 Python Scripts to Automate Your Daily Tasks in 2026

1. Bulk Rename Files in One Second

2. Send Automated Emails with Attachments

3. Scrape E-Commerce Prices

4. Auto-Generate a PDF Report

5. Monitor a Website and Get Alerts

6. Clean and Deduplicate a CSV File

7. Batch Download Files from the Web

Conclusion

I built a set of free tools !!!

1. 📧 Email & Phone Scraper

2. 🌍 IP Geolocation Tool

3. 📊 JSON / CSV → Excel Converter

4. 📱 QR Code Generator

Stack

Try it

Stop Getting Blocked: Recon Your Target Website Before Scraping It

The problem

Install

Usage

What it detects

Two engines

If you get blocked

Links